Uncertainty-aware artery/vein classification on retinal images
Authors: Galdran, A., Meyer, M., Costa, P., Mendonca and Campilho, A.
Journal: Proceedings - International Symposium on Biomedical Imaging
The automatic differentiation of retinal vessels into arteries and veins (A/V) is a highly relevant task within the field of retinal image analysis. However, due to limitations of retinal image acquisition devices, specialists can find it impossible to label certain vessels in eye fundus images. In this paper, we introduce a method that takes into account such uncertainty by design. For this, we formulate the A/V classification task as a four-class segmentation problem, and a Convolutional Neural Network is trained to classify pixels into background, A/V, or uncertain classes. The resulting technique can directly provide pixelwise uncertainty estimates. In addition, instead of depending on a previously available vessel segmentation, the method automatically segments the vessel tree. Experimental results show a performance comparable or superior to several recent A/V classification approaches. In addition, the proposed technique also attains state-of-the-art performance when evaluated for the task of vessel segmentation, generalizing to data that was not used during training, even with considerable differences in terms of appearance and resolution.
UNCERTAINTY-AWARE ARTERY/VEIN CLASSIFICATION ON RETINAL IMAGES
Authors: Galdran, A., Meyer, M., Costa, P., Mendonca, Campilho, A. and IEEE
Journal: 2019 IEEE 16TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2019)
Source: Web of Science (Lite)